Computer technology has advanced greatly in recent years, allowing the uses for computers to similarly grow. One such use is the storage of images. Databases of images that are accessible to computers are constantly expanding and cover a wide range of areas, including stock images that are made commercially available, images of art collections (e.g., by museums), etc. However, as the number of such images being stored has increased, so too has the difficulty in managing the retrieval of such images. Often times it is difficult for a user to search databases of such images to identify selected ones of the thousands of images that are available.
One difficulty in searching image databases is the manner in which images are stored versus the manner in which people think about and view images. It is possible to extract various low-level features regarding images, such as the color of particular portions of an image and shapes identified within an image, and make those features available to an image search engine. However, people don't tend to think of images using such low-level features. For example, a user that desires to retrieve images of brown dogs would typically not be willing and/or able to input search parameters identifying the necessary color codes and particular areas including those color codes, plus whatever low-level shape features are necessary to describe the shape of a dog in order to retrieve those images. Thus, there is currently a significant gap between the capabilities provided by image search engines and the usability desired by people using such engines.
One solution is to provide a text-based description of images. In accordance with this solution, images are individually and manually categorized by people, and various descriptive words for each image are added to a database. For example, a picture of a brown dog licking a small boy's face may include key words such as dog, brown, child, laugh, humor, etc. There are, however, problems with this solution. One such problem is that it requires manual categorization—an individual(s) must take the time to look at a picture, decide which key words to include for the picture, and record those key words. Another problem is that such a process is subjective. People tend to view images in different ways, viewing shapes, colors, and other features differently. With such a manual process, the key words will be skewed towards the way the individual cataloging the images views the images, and thus different from the way many other people will view the images.
The invention described below addresses these disadvantages, providing for improved image retrieval based on relevance feedback.